☆ 3.8 Proceedings Paper

SVM and ANN classification using GLCM and HOG features for COVID-19 and Pneumonia detection from Chest X-rays

2021 THIRD SOUTH AMERICAN COLLOQUIUM ON VISIBLE LIGHT COMMUNICATIONS (SACVLC 2021) (2021)

Exploring the effect of image enhancement techniques on COVID-19 detection using chest X-ray images

Tawsifur Rahman et al.

Summary: Computer-aided diagnosis for fast and reliable detection of COVID-19 using a large X-ray dataset, various image enhancement techniques, and deep learning models has shown promising results in this study.

COMPUTERS IN BIOLOGY AND MEDICINE (2021)

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Article Computer Science, Information Systems

Detecting SARS-CoV-2 From Chest X-Ray Using Artificial Intelligence

Md Manjurul Ahsan et al.

Summary: The study proposes and tests six modified deep learning models to detect SARS-CoV-2 infection from chest X-ray images, with VGG16 and MobileNetV2 showing promising results in accurately identifying patients with COVID-19 symptoms on both datasets. The poorly performing models in Study One significantly improved their accuracy when trained with the larger dataset in Study Two, while models like InceptionResNetV2 and VGG19 demonstrated high accuracy on both datasets, providing an accessible alternative to identify patients with COVID-19.

IEEE ACCESS (2021)

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Article Computer Science, Information Systems

A Review on Deep Learning Techniques for the Diagnosis of Novel Coronavirus (COVID-19)

Md. Milon Islam et al.

Summary: This paper provides an overview of systems developed for COVID-19 diagnosis using deep learning techniques, focusing on medical imaging modalities like CT and X-ray. It discusses well-known datasets used for training networks, data partitioning techniques, and performance measures. The paper concludes by addressing challenges and future trends in utilizing deep learning methods for COVID-19 detection. The aim is to aid experts and technicians in understanding and potentially further utilizing deep learning techniques in combating the pandemic.

IEEE ACCESS (2021)

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Article Radiology, Nuclear Medicine & Medical Imaging

Using Artificial Intelligence to Detect COVID-19 and Community-acquired Pneumonia Based on Pulmonary CT: Evaluation of the Diagnostic Accuracy

Lin Li et al.

RADIOLOGY (2020)

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Proceedings Paper Computer Science, Artificial Intelligence

X-ray Testing by Computer Vision

Domingo Mery

2013 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS (CVPRW) (2013)

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SVM and ANN classification using GLCM and HOG features for COVID-19 and Pneumonia detection from Chest X-rays

相关参考文献

Exploring the effect of image enhancement techniques on COVID-19 detection using chest X-ray images

Detecting SARS-CoV-2 From Chest X-Ray Using Artificial Intelligence

A Review on Deep Learning Techniques for the Diagnosis of Novel Coronavirus (COVID-19)

Using Artificial Intelligence to Detect COVID-19 and Community-acquired Pneumonia Based on Pulmonary CT: Evaluation of the Diagnostic Accuracy

X-ray Testing by Computer Vision

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